Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19

Association between intensive blood pressure lowering and stroke-free survival among patients with and without Diabetes

This study pooled data from SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD-BP (Action to Control Cardiovascular Risk in Diabetes Blood Pressure) trial to estimate the treatment effect of intensive BP on stroke prevention, and investigate whether stroke risk score impacted treatment effect. Of all the potential manifestations of the hypertension, the most severe outcomes were stroke or death. A composite endpoint of time to death or stroke (stroke-free survival [SFS]), whichever occurred first, was defined as the outcome of interest. Participants without prevalent stroke were stratified into stroke risk tertiles based on the predicted revised Framingham Stroke Risk Score. The stratified Cox model was used to calculate the hazard ratio (HR) for the intensive BP treatment. 834 (5.92%) patients had SFS events over a median follow-up of 3.68 years. A reduction in the risk for SFS was observed among the intensive BP group as compared with the standard BP group (HR: 0.76, 95% CI: 0.65, 0.89; risk difference: 0.98([0.20, 1.76]). Further analyses demonstrated the significant benefit of intensive BP treatment on SFS only among participants having a high stroke risk (risk tertile 1: 0.76 [0.52, 1.11], number needed to treat [NNT] = 861; risk tertile 2: 0.87[0.65, 1.16], NNT = 91; risk tertile 3: 0.69[0.56, 0.86], NNT = 50). Intensive BP treatment lowered the risk of SFS, particularly for those at high risk of stroke.

Brief report on the development of hemorrhagic pericardial effusion after thoracic surgery for traumatic injuries

Introduction

Hemorrhagic pericardial effusion (HPE) is a rare but life-threatening diagnosis that may occur after thoracic trauma. Previous reports have concentrated on delayed HPE in those who did not require initial surgical intervention for their traumatic injuries. In this report, we identify and characterize the phenomenon of HPE after emergent thoracic surgery for trauma.

Methods

This is a retrospective review of patients who required emergent thoracic surgery for trauma at a level 1 trauma center from 2017 to 2021. Using the institutional trauma database, demographics, injury characteristics, and outcomes were compared between patients with HPE and those without HPE after thoracic surgery for trauma.

Results

Ninety-one patients were identified who underwent emergent thoracic surgery for trauma. Most were young men who sustained a penetrating thoracic injury. Seven patients (7.7%) went on to develop HPE. Patients who developed HPE were younger (18 vs. 32 years, p=0.034), required bilateral anterolateral thoracotomy (85% vs. 7%, p<0.001), and were more likely to have pulmonary injuries (100% vs. 52.4%, p<0.001). Five patients with HPE survived to hospital discharge. The two patients with HPE who died were both coagulopathic and had HPE diagnosed within 4 days of injury. The median time to HPE diagnosis in survivors was 24 days with four of five HPE survivors on therapeutic anticoagulation at the time of diagnosis.

Conclusions

HPE may occur after emergent thoracic surgery for trauma. Those at highest risk of HPE include younger patients with bilateral thoracotomy incisions and pulmonary injuries. Early HPE, clinical signs of tamponade, and/or coagulopathy in patients with HPE portend a worse prognosis. Surgeons and trauma team members caring for patients after emergent thoracic exploration for trauma should be aware of this potentially devastating complication and should consider postoperative echocardiography in high-risk patients.

Quantifying Treatment Effects in Trials with Multiple Event-Time Outcomes

BACKGROUND

Data on the occurrence times of multiple outcomes, reflecting the temporal profile of disease burden/progression, have been used to estimate treatment effects in various recent randomized trials. Most procedures for analyzing these data require specific model assumptions. When the assumptions are not met, the results may be misleading. Robust, model-free procedures for study design and analysis that enable clinically meaningful interpretations are warranted.

METHODS

For each treatment group, we constructed and summarized the estimated mean cumulative count of events over time by the area under the curve (AUC), which can be interpreted as the mean total event-free time lost from multiple undesirable outcomes. A higher curve, and resulting larger AUC, implies a worse treatment. The treatment effect is quantified by the ratio and/or difference of AUCs. The timing and occurrence of recurrent heart failure hospitalizations (HFHs) and cardiovascular (CV) death from Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF), comparing sacubitril/valsartan with valsartan, are presented for illustration. We also discuss the design of future studies on the basis of the proposed method.

RESULTS

With 48 months of follow-up, estimated AUCs, representing the total event-free time lost to HFHs and CV death, were 11.3 and 13.1 event-months for sacubitril/valsartan and valsartan, respectively. The ratio of these AUCs was 0.86 (95% confidence interval, 0.75 to 1.00; P=0.049), a 14% reduction of disease burden favoring combination therapy. A future study, similar to PARAGON-HF, designed using the new proposal would require fewer patients would than a conventional time-to-first-event analysis.

CONCLUSIONS

The proposed method is robust and model-free and provides a clinically interpretable, time-scale summary of the treatment effect. (Funded by National Institutes of Health.).

Evaluating Primary Endpoints for COVID-19 Therapeutic Trials to Assess Recovery

Rationale: Uncertainty regarding the natural history of coronavirus disease (COVID-19) led to difficulty in efficacy endpoint selection for therapeutic trials. Capturing outcomes that occur after hospital discharge may improve assessment of clinical recovery among hospitalized patients with COVID-19. Objectives: Evaluate 90-day clinical course of patients hospitalized with COVID-19, comparing three distinct definitions of recovery. Methods: We used pooled data from three clinical trials of neutralizing monoclonal antibodies to compare: 1) the hospital discharge approach; 2) the TICO (Therapeutics for Inpatients with COVID-19) trials sustained recovery approach; and 3) a comprehensive approach. At the time of enrollment, all patients were hospitalized in a non-ICU setting without organ failure or major extrapulmonary manifestations of COVID-19. We defined discordance as a difference between time to recovery. Measurements and Main Results: Discordance between the hospital discharge and comprehensive approaches occurred in 170 (20%) of 850 enrolled participants, including 126 hospital readmissions and 24 deaths after initial hospital discharge. Discordant participants were older (median age, 68 vs. 59 years; P < 0.001) and more had a comorbidity (84% vs. 70%; P < 0.001). Of 170 discordant participants, 106 (62%) had postdischarge events captured by the TICO approach. Conclusions: Among patients hospitalized with COVID-19, 20% had clinically significant postdischarge events within 90 days after randomization in patients who would be considered "recovered" using the hospital discharge approach. Using the TICO approach balances length of follow-up with practical limitations. However, clinical trials of COVID-19 therapeutics should use follow-up times up to 90 days to assess clinical recovery more accurately.

Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

SARS-CoV-2 Vaccines: clinical endpoints and psychological perspectives: A literature review

Background

About 270 million cases have been confirmed, and 5.3 million fatalities Worldwide due to SARS-CoV-2. Several vaccine candidates have entered phase 3 of the clinical trial and are being investigated to provide immunity to the maximum percentage of people. A safe and effective vaccine is required to tackle the current COVID-19 waves. There have been reports that clinical endpoints and psychological parameters are necessary to consider vaccine efficacy. This review examines the clinical endpoints required for a successful SARS-CoV-2 vaccine and the influences of psychological parameters on its efficacy.

Methods

The main research question was to find out the clinical endpoints that determine the vaccine efficacy? And what kind of psychological parameters affect the vaccine efficacy? The information was taken from several journals, databases, and scientific search engines like Googe scholar, Pubmed, Scopus, Web of Science, Science direct, WHO website, and other various sites. The research studies were searched using keywords; SAR-CoV-2 vaccine efficacy, psychological effect on SARS-CoV-2 vaccine, SARS-CoV-2 vaccine endpoints.

Results

This review has highlighted various clinical endpoints that are the main determinants of clinical vaccine efficacy. Currently, vaccinations are being carried out throughout the world; it is important to investigate the main determinants affecting vaccine efficacy. We have focused on the clinical endpoints and the influence of psychological parameters that affect the vaccine efficacy in clinical settings. The primary endpoints include the risk of infection, symptoms, and severity of COVID-19, while hospitalization length, supplemental oxygen requirement, and mechanical ventilation are secondary endpoints in the clinical endpoints. Some tangential endpoints were also considered, including organ dysfunction, stroke, and MI. Many psychological associated things have influenced the vaccine efficacy, like the lower antibody titers in the vaccinated people. In addition to that, Short- and long-term stress and sleep deprivation were also found to affect the vaccine efficacy.

Conclusion

The review summarizes the important clinical endpoints required for a successful vaccine candidate. In addition to primary and secondary endpoints, auxiliary endpoints and the disease burden also play an important role in modulating vaccine efficacy. Moreover, the psychological perspective also influences vaccine efficacy. Effective follow-up of participants should follow to examine the clinical endpoints to reach any conclusion about vaccine efficacy.

Patient and Citizen Participation in the Identification of Ethical Considerations Aiming to Address Uncertainty in the Evaluation of Promising Interventions in a Pandemic Context

Since the beginning of the COVID-19 pandemic, numerous studies have been conducted to identify interventions that could contribute to alleviating the burden it has caused. The Institut national d'excellence en santé et en services sociaux (INESSS) has played a key role in informing the government of Québec regarding the evaluation of specific pandemic-related interventions. This process took place in a context characterized by a sense of urgency to assess and recommend potential interventions that could save lives and reduce the effects of the disease on populations and healthcare systems, which increased the pressure on the regulatory agencies leading these evaluations. While some of the interventions examined were considered promising, results from COVID-19 studies often led to uncertainty regarding their efficacy or safety. Regulatory agencies evaluating the value of promising interventions thus face challenges in deciding whether these should be made available to the population, particularly when assessing their benefit-risk balance. To shed light on these challenges, we identified underlying ethical considerations that can influence such an assessment. A rapid literature review was conducted in February 2021, to identify the main challenges associated with the benefit-risk balance assessment of promising interventions. To reinforce our understanding of the underlying ethical considerations, we initiated a discussion among various social actors involved in critical thinking surrounding the evaluation of promising interventions, including ethicists, clinicians and researchers involved in clinical or public health practice, as well as patients and citizens. This discussion allowed us to create a space for exchange and mutual understanding among these various actors who contributed equally to the identification of ethical considerations. The knowledge and perspectives stemming from the scientific literature and those consulted were integrated in a common reflection on these ethical considerations. This allowed patients and citizens, directly affected by the evaluation of pandemic-related interventions and the resulting social choices, to contribute to the identification of the relevant ethical considerations. It also allowed for reflection on the responsibilities of the various actors involved in the development, evaluation, and distribution of promising interventions in a setting of urgency and uncertainty, such as that brought about by the COVID-19 pandemic.

Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing Risks: A Review

Importance

In a comparative trial, the time to a clinical event is often a key end point. However, the occurrence of a terminal event, such as death or premature study discontinuation, may preclude observation of this outcome. Although various methods for handling competing risks are available, no specific recommendations have been made for scenarios encountered in practice, especially when the terminal event profiles of the study arms are dissimilar. Moreover, appropriate methods for a desirable outcome, such as live hospital discharge, have seldom been discussed.

Observations

Several of the most commonly used methods are reviewed. The first regards the terminal event as censoring and applies standard survival analysis to the event of interest. The between-group difference is usually summarized by the cause-specific hazard ratio. This summary measure is inappropriate when the new therapy markedly prolongs time to the terminal event. Moreover, the corresponding Kaplan-Meier curve for the end point of interest is uninterpretable. The second method is to use the cumulative incidence curve, which is the probability of experiencing the event of interest by each time point, acknowledging that patients who have died will never experience the event. However, the resulting pseudo hazard ratio is difficult to interpret. With a proper alternative summary measure, this approach works well for a desirable outcome but may not for an undesirable outcome. The third method focuses on the event-free survival time by combining information from occurrences of the terminal event and the event of interest simultaneously. This clinically interpretable method naturally accounts for differences in terminal event rates when comparing treatments with respect to the time to an undesirable outcome.

Conclusions and Relevance

This article enhances our understanding of each method's advantages and shortcomings and assists practitioners in choosing appropriate methods for handling competing risk problems in practice.

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

BACKGROUND

There are several challenges in designing clinical trials for the treatment of novel infectious diseases, such as COVID-19. In particular, the definition of endpoints related to the severity, time frame, and clinical course remains unclear. Therefore, we conducted a cross-sectional analysis of phase III randomized trials for COVID-19 registered at ClinicalTrials.gov .

METHODS

We collected the data from ClinicalTrials.gov on March 31, 2021, by specifying the following search conditions under Advanced Search: Condition or disease: (COVID-19) OR (SARS-CoV-2); Study type: Interventional Studies; Study Results: All Studies; Recruitment: Not yet recruiting, Recruiting, Enrolling by invitation, Active, Not recruiting, Suspended, Completed; Sex: All; and Phase: Phase 3. From the downloaded search results, we selected trials that met the following criteria: Primary Purpose: Treatment; Allocation: Randomized. We manually transcribed information not included in the downloaded file, such as Primary Outcome Measures, Secondary Outcome Measures, Time Frame, and Inclusion Criteria. In the analysis, we examined primary and secondary endpoints in trials with severe and non-severe patients, including the types of endpoints, time frame, clinical course, and sample size.

RESULTS

A total of 406 trials were included in the analysis. The median numbers of endpoints in trials with severe and non-severe patients were 9 and 7, respectively. Approximately 25% of the trials used multiple primary endpoints. Regardless of the type of endpoint, the time frames were longer in the trials with severe patients than in the trials with non-severe patients. In the evaluation of the clinical course, worsening was often considered in binary endpoints, and improvement was considered in time-to-event endpoints. The sample size was the largest in clinical trials using binary endpoints.

CONCLUSIONS

Endpoints can differ with respect to severity, and the clinical course and time frame are important for defining endpoints. This study provides information that can facilitate the achievement of a consensus for the endpoints in evaluating COVID-19 treatments.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19

The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.